19 citations · 30 across the 6 of their papers we have counts for
6 papers
On the Joint Interaction of Models, Data, and Features
Yiding Jiang, Christina Baek, J. Zico Kolter
Learning features from data is one of the defining characteristics of deep learning, but our theoretical understanding of the role features play in deep learning is still rudimenta…
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift
Christina Baek, Yiding Jiang, Aditi Raghunathan +1
Recently, Miller et al. showed that a model's in-distribution (ID) accuracy has a strong linear correlation with its out-of-distribution (OOD) accuracy on several OOD benchmarks --…
Efficient Maximal Coding Rate Reduction by Variational Forms
Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan +3
The principle of Maximal Coding Rate Reduction (MCR) has recently been proposed as a training objective for learning discriminative low-dimensional structures intrinsic to high…
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning
Yuexiang Zhai, Christina Baek, Zhengyuan Zhou +2
Many goal-reaching reinforcement learning (RL) tasks have empirically verified that rewarding the agent on subgoals improves convergence speed and practical performance. We attempt…
Assessing Generalization of SGD via Disagreement
Yiding Jiang, Vaishnavh Nagarajan, Christina Baek +1
We empirically show that the test error of deep networks can be estimated by simply training the same architecture on the same training set but with a different run of Stochastic G…
Incremental Learning via Rate Reduction
Ziyang Wu, Christina Baek, Chong You +1
Current deep learning architectures suffer from catastrophic forgetting, a failure to retain knowledge of previously learned classes when incrementally trained on new classes. The…